{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning\n",
    "\n",
    "With certain data types it is possible to use the weights learned in one task to be **transferred** to another task. For example in a task that is used to detect Animals and Vehicles in images (as done in CIFAR10) could be reused to classify dogs and cats. \n",
    "\n",
    "Transfer Learning is heavily used in Image recognition and Natural Language Processing (NLP) related tasks.\n",
    "\n",
    "This tutorial is based on https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.\n",
    "![alt-text](./doge.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /root/miniconda3/lib/python3.6/site-packages\n",
      "Fetching package metadata ...........\n",
      "Solving package specifications: .\n",
      "\n",
      "# All requested packages already installed.\n",
      "# packages in environment at /root/miniconda3:\n",
      "#\n",
      "pillow                    4.2.1                    py36_0  \n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm\n",
    "!conda install -y Pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir, getsize\n",
    "from os import mkdir, makedirs, remove\n",
    "from tqdm import tqdm\n",
    "\n",
    "import zipfile\n",
    "import pickle\n",
    "\n",
    "from keras.models import Sequential, Model\n",
    "from keras import optimizers\n",
    "from keras.layers import Dense, Activation, Conv2D, MaxPool2D, Flatten, BatchNormalization, Dropout\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "import glob\n",
    "import shutil\n",
    "\n",
    "import pickle\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and extract the doge and cate pictures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catdog_dataset_folder_path = 'catdog'\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile('catdog.zip'):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Doge n Cate Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip',\n",
    "            'catdog.zip',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(catdog_dataset_folder_path):\n",
    "    mkdir(catdog_dataset_folder_path)\n",
    "    with zipfile.ZipFile('catdog.zip') as f:\n",
    "        f.extractall('./'+catdog_dataset_folder_path)\n",
    "        # Unfortunately some of the files are corrupt so we need to clean these out:\n",
    "        !apt-get install -y jhead > /dev/null 2>&1\n",
    "        !jhead -de catdog/PetImages/Cat/*.jpg > /dev/null 2>&1 \n",
    "        !jhead -de catdog/PetImages/Dog/*.jpg > /dev/null 2>&1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files = glob.glob(catdog_dataset_folder_path+'/PetImages/**/*.jpg')\n",
    "labels = np.array([0]*12500+[1]*12500)\n",
    "\n",
    "size = np.zeros(len(files))\n",
    "for i,f in enumerate(files):\n",
    "    size[i] = getsize(f)\n",
    "    \n",
    "idx = np.where(size==0)[0]\n",
    "for i in idx[::-1]:\n",
    "    del files[i]\n",
    "    labels = np.delete(labels, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In keras we are required to place the training images in a certain folder, with the subfolders structured so that each subfolder contains the class. We will structure the validation folder in the same way:\n",
    "```\n",
    "data/\n",
    "    train/\n",
    "        dogs/\n",
    "            dog001.jpg\n",
    "            dog002.jpg\n",
    "            ...\n",
    "        cats/\n",
    "            cat001.jpg\n",
    "            cat002.jpg\n",
    "            ...\n",
    "    validation/\n",
    "        dogs/\n",
    "            dog001.jpg\n",
    "            dog002.jpg\n",
    "            ...\n",
    "        cats/\n",
    "            cat001.jpg\n",
    "            cat002.jpg\n",
    "            ...\n",
    "```            \n",
    "\n",
    "From the dataset we randomly choose 20000 images and moves them to training and the rest to testing folders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len_data = len(files)\n",
    "train_examples = 20000\n",
    "test_examples = len_data - train_examples\n",
    "\n",
    "# randomly choose 20000 as training and testing cases\n",
    "permutation = np.random.permutation(len_data)\n",
    "train_set = [files[i] for i in permutation[:][:train_examples]]\n",
    "test_set = [files[i] for i in permutation[-test_examples:]]\n",
    "train_labels = labels[permutation[:train_examples]]\n",
    "test_labels = labels[permutation[-test_examples:]]\n",
    "\n",
    "train_folder = catdog_dataset_folder_path+'/train'\n",
    "test_folder = catdog_dataset_folder_path+'/test'\n",
    "\n",
    "if isdir(train_folder): #if directory already exists\n",
    "    shutil.rmtree(train_folder)    \n",
    "if isdir(test_folder): #if directory already exists\n",
    "    shutil.rmtree(test_folder)    \n",
    "makedirs(train_folder+'/cat/')\n",
    "makedirs(train_folder+'/dog/')\n",
    "makedirs(test_folder+'/cat/')\n",
    "makedirs(test_folder+'/dog/')\n",
    "\n",
    "for f,i in zip(train_set, train_labels):\n",
    "    if i==0:\n",
    "        shutil.copy2(f, train_folder+'/cat/')\n",
    "    else:\n",
    "        shutil.copy2(f, train_folder+'/dog/')\n",
    "        \n",
    "for f,i in zip(test_set, test_labels):\n",
    "    if i==0:\n",
    "        shutil.copy2(f, test_folder+'/cat/')\n",
    "    else:\n",
    "        shutil.copy2(f, test_folder+'/dog/')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View some sample images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "image/png": 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3aLcq87eH+h1vHUo8IB6WZ5ZKy7FZQ96V/bbEUdouMJEZlWcTHkY9HY/uJIHmDySQEyrH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff4eb4bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MIsirYNs2LDsIqLqR3HTAz7KsDIDPAvhFz/OMp+p5ngcgGA6WcR+0LOuIZVlH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\nM6J1t+3eilaJxmVv90hY0kOw0PatAk6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SPLMs+Qo/9Kv/FgBw1ccYPb4gc5hk6/JSSdYj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff54650ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MLQioWcQXSc/J/pYX50Pr2IQRsdLymaupJ/uXoKf9v+xbyD5yXmySjxyLrGOn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\njsad6ycvZWXmSOyxFzK0nSPPUEHS7BRlDmxpMTkv+TxF909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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff4b34e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2ijn92E2bnXLvg+qcyTGRFXCaeVWa3HAxKjJhp0apysii2lb8whf2qnrsAaSW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\nalXHYcdq6Act26UbWp4cO9f6P1b7v3LmbOY1NXPH6yX3Wh/IPRzg5XZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff4b281668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7NjL9pOjGag8yj/y5b4jCTPNuNG7u8/0vv0WrZr0seioVqTS6No+9vMZsjGnJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff4b2156a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RvwjTvh0Lcash69zrKCCInxl/GDEL5sLE9X4JjbkbxV/Ce0sp5ajKoRN4FvP8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff4b290cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        rotation_range=5,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "img_height = img_width = 100\n",
    "channels = 3\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=1,\n",
    "    class_mode=None)\n",
    "\n",
    "i = 0\n",
    "img_list = []\n",
    "for batch in train_generator: #.flow(x, batch_size=1)\n",
    "    img_list.append(batch)\n",
    "    i += 1\n",
    "    if i > 5:\n",
    "        break\n",
    "        \n",
    "\n",
    "for img in img_list:\n",
    "    plt.imshow(np.squeeze(img))\n",
    "    plt.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic logistic multiclass classification:\n",
    "\n",
    "Always, ALWAYS compare to the most basic possible ML/ statistics algorithm. In this case logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1000\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, y_train = next(train_generator)\n",
    "x_test, y_test = next(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic = LogisticRegression()\n",
    "logistic.fit(x_train.reshape(batch_size,-1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = logistic.predict(x_test.reshape(len(x_test), -1))\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting the probabilities for the first 3 images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45270886,  0.54729114],\n",
       "       [ 0.44448103,  0.55551897],\n",
       "       [ 0.00495017,  0.99504983]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.predict_proba(x_test[:3].reshape(3,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy of the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.514"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_pred == y_test)/len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolution Neural Networks (CNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "\n",
    "# TODO: Add a CNN:\n",
    "# Note 1: The input_shape needs to be specified in this case (input_height, input_width, channels)\n",
    "# Note 2: The order usually goes Conv2D, Activation, MaxPool,\n",
    "# Note 3: Must be flattened before passing onto Dense layers\n",
    "# Note 4: The loss is binary_crossentropy\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(optimizer='adadelta', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 100, 100, 8)       224       \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 100, 100, 8)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 33, 33, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 33, 33, 16)        1168      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 33, 33, 16)        64        \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 33, 33, 16)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 16, 16, 32)        4640      \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 16, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 8, 32)          9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 8, 8, 32)          128       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 16,113.0\n",
      "Trainable params: 15,953.0\n",
      "Non-trainable params: 160.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Epoch 1/2\n",
      "156/156 [==============================] - 387s - loss: 0.6443 - acc: 0.6396   \n",
      "Epoch 2/2\n",
      "156/156 [==============================] - 336s - loss: 0.5731 - acc: 0.6981   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f82935d40b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')\n",
    "model.fit_generator(train_generator, train_examples//batch_size, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1\n",
    "test_generator = datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary', \n",
    "    shuffle=False)\n",
    "y_pred = model.predict_generator(test_generator, test_examples//batch_size, workers=4)\n",
    "# model.predict_classes(test_x)\n",
    "# np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct predictions: 0.5070028011204482\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for i, f in enumerate(test_generator.filenames):\n",
    "    if f.startswith('cat') and y_pred[i]<0.5:\n",
    "        correct +=1\n",
    "    if f.startswith('dog') and y_pred[i]>=0.5:\n",
    "        correct +=1\n",
    "\n",
    "print('Correct predictions: '+str(correct/len(test_generator.filenames)))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 6\n",
    "test_generator = datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    color_mode = \"rgb\",\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary', \n",
    "    shuffle=True)\n",
    "x_test, y_test = next(test_generator)\n",
    "\n",
    "p = model.predict(x_test)\n",
    "p = np.hstack([y_pred, 1-y_pred])\n",
    "label_dict = {0: 'cat', 1: 'dog'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bReaCUqHCsDfB0Axm8xG5TJAEuF5OGE0ZDI/J8hDLFggBS0sN0iTHK5SYTidkWU6aSio1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RsEBRTKjqko21TdpJzFKvxdrKJcbjDV796teRZSlRFNFuxYThEq/5utcyHo9ZXjjILS/9WpSt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\nMzGxg2QySTqdxuPx4Pf78Xg87Nq1q5Nnizb1ep0jR44Q7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Wa8NDdMsx0I6cDniTPevxPL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\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RjUWol0tYhsljTz7J5ekLFA2Ltdw6mc0NPvboc4z3jiHZGmMDu9kzOMDU/j3IEZsLZ8+x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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f826a23a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "for i in range(batch_size):\n",
    "    plt.subplot(batch_size,2,2*i+1)\n",
    "    plt.imshow(x_test[i])\n",
    "    plt.title(label_dict[y_test[i]])\n",
    "    \n",
    "    plt.subplot(batch_size,2,2*i+2)\n",
    "    plt.bar(range(2),p[i])\n",
    "    plt.xticks(range(2), ['cat', 'dog'])\n",
    "#     plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28162625,  0.71837378],\n",
       "       [ 0.24552229,  0.75447774],\n",
       "       [ 0.15673721,  0.84326279],\n",
       "       ..., \n",
       "       [ 0.36343947,  0.63656056],\n",
       "       [ 0.29009002,  0.70990998],\n",
       "       [ 0.27991441,  0.72008562]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning - Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "model = applications.VGG16(include_top=False, input_shape=(img_width, img_height, channels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, None, None, 3)     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "=================================================================\n",
      "Total params: 14,714,688.0\n",
      "Trainable params: 14,714,688.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = applications.VGG16(include_top=False, weights='imagenet')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Do not _uncomment_ and run the following two blocks unless absolutely necessary**. It takes almost one hour to run. It took me a while to understand why in the Keras blog they had saved the parameters. It isn't necassary for you to save it. However, if you do come back to the tutorial you probably dont want to run this section again. It is slow mainly because there's 14 Million parameters to go through for each example. Having a GPU in this instance would help tremendously.\n",
    "\n",
    "### Note 1\n",
    "It is however important to notice that I am **not** training in this block. I am predicting using a truncated VGG16 net. See how I set the `include_top=False` parameter above. VGG16 was originally trained on the CIFAR10 dataset so that it would predict 10 classes. Now that we are truncating it and only using all but the top few layers (lyer closes to the prediction), it outputs a (3,3,512) image in our case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=None,\n",
    "    shuffle=False)\n",
    "# bottleneck_features_train = model.predict_generator(generator, train_examples//batch_size, verbose=1, workers=4)\n",
    "# pickle.dump(bottleneck_features_train, open('bottleneck_features_train.npy', 'wb'))\n",
    "# bottleneck_features_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "valid_generator = datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=None,\n",
    "    shuffle=False)\n",
    "# bottleneck_features_valid = model.predict_generator(generator, test_examples//batch_size, verbose=1, workers=4)\n",
    "# with open('bottleneck_features_valid.npy', 'wb') as f:\n",
    "#     pickle.dump(bottleneck_features_valid, f)\n",
    "# bottleneck_features_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bott"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               1179904   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,180,161.0\n",
      "Trainable params: 1,180,161.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "with open('bottleneck_features_train.npy','rb') as f:\n",
    "    bottleneck_features_train = pickle.load(f)\n",
    "\n",
    "model = Sequential()\n",
    "# TODO: Make a 1 hidden layer NN \n",
    "# Note 1: Add Flatten() layer The input shape is the bottleneck features dimension\n",
    "# Note 2: Choose a suitable dimension for the hidden layer (eg. half way between final node and dimension of input)\n",
    "# Note 3: Last layer is 1 with activation sigmoid (remember we are trying to predict a probability)\n",
    "# Note 4: See previous todo section to see what the loss is supposed to be (unless you know already of course)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Epoch 1/10\n",
      "19872/19872 [==============================] - 2s - loss: 0.6060 - acc: 0.7756     \n",
      "Epoch 2/10\n",
      "19872/19872 [==============================] - 2s - loss: 0.3599 - acc: 0.8418     \n",
      "Epoch 3/10\n",
      "19872/19872 [==============================] - 2s - loss: 0.3253 - acc: 0.8575     \n",
      "Epoch 4/10\n",
      "19872/19872 [==============================] - 2s - loss: 0.3053 - acc: 0.8682     \n",
      "Epoch 5/10\n",
      "19872/19872 [==============================] - 7s - loss: 0.2904 - acc: 0.8770     \n",
      "Epoch 6/10\n",
      "19872/19872 [==============================] - 9s - loss: 0.2796 - acc: 0.8807     \n",
      "Epoch 7/10\n",
      "19872/19872 [==============================] - 10s - loss: 0.2670 - acc: 0.8846    - ETA: 1s - loss: 0.2\n",
      "Epoch 8/10\n",
      "19872/19872 [==============================] - 10s - loss: 0.2530 - acc: 0.8934    \n",
      "Epoch 9/10\n",
      "19872/19872 [==============================] - 11s - loss: 0.2472 - acc: 0.8955    \n",
      "Epoch 10/10\n",
      "19872/19872 [==============================] - 12s - loss: 0.2371 - acc: 0.9004    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7eff4ac14c88>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "generator = datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=None,\n",
    "    shuffle=False)\n",
    "\n",
    "labels = np.array([0 if f.startswith('cat') else 1 for f in generator.filenames])[:len(bottleneck_features_train)]\n",
    "model.fit(bottleneck_features_train, labels, epochs=10, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000/4870 [=======================>......] - ETA: 0s\n",
      "The accuracy is: 0.8989733059548255\n"
     ]
    }
   ],
   "source": [
    "with open('bottleneck_features_valid.npy','rb') as f:\n",
    "    bottleneck_features_valid = pickle.load(f)\n",
    "valid_labels = np.array([0 if f.startswith('cat') else 1 for f in valid_generator.filenames])[:len(bottleneck_features_valid)]\n",
    "y_valid_pred = model.predict_classes(bottleneck_features_valid)\n",
    "accuracy = np.count_nonzero(valid_labels == y_valid_pred.ravel())/len(valid_labels)\n",
    "\n",
    "print('\\nThe accuracy is: '+str(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "img = Image.open('doge.jpg')\n",
    "img.thumbnail((img_height, img_width), Image.ANTIALIAS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning - Part 2\n",
    "\n",
    "We can refine the model further by adjusting the last convolutional layer.\n",
    "\n",
    "Note that vgg16 is of type `Model` and not `Sequential`. Hence we cannot `add` the top layer as suggested in the keras blog.\n",
    "\n",
    "### Note 2\n",
    "We are setting the trainable weights to be everything but the last convolutional layer and the fully connected (dense) layers. Take note of the number of trainable parameters in the summary below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vgg16 = applications.VGG16(include_top=False, weights='imagenet', input_shape=(img_width, img_height, channels))\n",
    "combinedModel = Model(inputs= vgg16.input, outputs= model(vgg16.output))\n",
    "\n",
    "for layer in combinedModel.layers[:-3]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 100, 100, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 100, 100, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 100, 100, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 50, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 50, 50, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 25, 25, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 25, 25, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 25, 25, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 12, 12, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 12, 12, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 12, 12, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 12, 12, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 1)                 1180161   \n",
      "=================================================================\n",
      "Total params: 15,894,849.0\n",
      "Trainable params: 3,539,969.0\n",
      "Non-trainable params: 12,354,880.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "combinedModel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can try to use the `adagrad` optmizer if you wish but you'll soon see that all the progress that was made in `model` will be undone. It will infact overwrite the weights in `model` and you would have to rerun the `model` training from the `bottleneck_features` section.\n",
    "\n",
    "Why? It's so that the updates are small and does not destablise the weights that were previously learnt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save_weights('fc_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20000 images belonging to 2 classes.\n",
      "Found 4998 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "combinedModel.compile(loss='binary_crossentropy',\n",
    "              optimizer = optimizers.RMSprop(lr=1e-4, decay=0.9), # optimizers.SGD(lr=1e-4, momentum=0.9)\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# prepare data augmentation configuration\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1. / 255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_folder,\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    test_folder,\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "156/156 [==============================] - 3369s - loss: 0.3049 - acc: 0.8695 - val_loss: 0.2076 - val_acc: 0.9149\n",
      "Epoch 2/5\n",
      "155/156 [============================>.] - ETA: 16s - loss: 0.2834 - acc: 0.8760"
     ]
    }
   ],
   "source": [
    "# fine-tune the model\n",
    "combinedModel.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_examples//batch_size,\n",
    "    epochs=5,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=test_examples//batch_size) # len(valid_generator.filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      " 59/156 [==========>...................] - ETA: 1636s - loss: 0.3259 - acc: 0.8567"
     ]
    }
   ],
   "source": [
    "# fine-tune the model\n",
    "combinedModel.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_examples//batch_size,\n",
    "    epochs=5,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=test_examples//batch_size) # len(valid_generator.filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "img = Image.open('doge.jpg')\n",
    "img = np.asarray(img.resize((img_height, img_width), Image.ANTIALIAS))/255\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "p = combinedModel.predict(np.array([img]))\n",
    "print('The probability that this is a doge is: ' +str(p[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img = Image.open('grumpy_cat.jpeg')\n",
    "img = np.asarray(img.resize((img_height, img_width), Image.ANTIALIAS))/255\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "p = combinedModel.predict(np.array([img]))\n",
    "print('The probability that this is a doge is: ' +str(p[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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